Metabolic shifts during coffee consumption refresh the immune response: insight from comprehensive multiomics analysis

Abstract Coffee, a widely consumed beverage, has shown benefits for human health but lacks sufficient basic and clinical evidence to fully understand its impacts and mechanisms. Here, we conducted a cross‐sectional observational study of coffee consumption and a 1‐month clinical trial in humans. We found that coffee consumption significantly reshaped the immune system and metabolism, including reduced levels of inflammatory factors and a reduced frequency of senescent T cells. The frequency of senescent T cells and the levels of the senescence‐associated secretory phenotype were lower in both long‐term coffee consumers and new coffee consumers than in coffee nondrinking subjects, suggesting that coffee has anti‐immunosenescence effects. Moreover, coffee consumption downregulated the activities of the The Janus kinase/signal transduction and activator of transcription (JAK/STAT) and mitogen‐activated protein kinases (MAPK) signaling pathways and reduced systemic proinflammatory cytokine levels. Mechanistically, coffee‐associated metabolites, such as 1‐methylxanthine, 3‐methylxanthine, paraxanthine, and ceramide, reduced the frequency of senescent CD4+CD57+ T cells in vitro. Finally, in vivo, coffee intake alleviated inflammation and immunosenescence in imiquimod‐induced psoriasis‐like mice. Our results provide novel evidence of the anti‐inflammatory and anti‐immunosenescence effects of coffee, suggesting that coffee consumption could be considered a healthy habit.


Supplementary Materials
Metabolic shifts during coffee consumption refresh immune response: Insight from comprehensive multi-omics analysis

Flow cytometry analysis
Peripheral blood mononuclear cells (PBMCs) from Human were separated by Ficoll-Paque Plus (GE healthcare, cat. 17-1440-03) to get a single-cell suspension.About 2×10 6 cells were suspended in PBS and incubated with human Fc-R block (BD Pharmingen, 564765) at room temperature for 10 minutes, and then incubated with antibodies against surface markers at 4°C for 45 minutes in the dark.

RNA isolation and RT-qPCR
Total RNA was extracted using TRIzol reagent (Invitrogen).RNA quality control and concentration were detected by NanoDrop spectrophotometer (ND-2000, Thermo).

RNA-seq and bioinformatics analysis
The Novogene Company conducted the RNA extraction, sample detection, enrichment, amplification, library preparation, and Illumina sequencing.R Studio was employed for the analysis of all RNA-seq data.Differential gene expression analysis was performed using the 'limma' R package.Gene Ontology enrichment analysis was carried out using the cluster Profiler package.The analyses were based on a significance threshold of p-value < 0.05 and fold-change values > 1.5-fold to identify differentially expressed genes.

Sample preparations for metabolome profiling of human serum and stool
For serum analysis, 100 μL of serum sample was transferred to an Eppendorf tube and mixed with 300 μL of extraction solution (methanol) containing the internal standard L-2-Chlorophenylalanine (2 pg/mL).After 30 seconds of vortexing, the samples were sonicated for 10 minutes in an ice-water bath.Subsequently, the samples were incubated at -40°C for 1 hour and centrifuged at 12,000 rpm for 15 minutes at 4°C.
Following centrifugation, 100 μL of the supernatant was transferred to a fresh glass vial for LC-MS analysis.
After 30 seconds of vortexing, the samples were homogenized at 40 Hz for 4 minutes and then sonicated for 5 minutes in an ice-water bath.This homogenization and sonication cycle was repeated three times.The samples were then incubated at -40°C for 1 hour and centrifuged at 10,000 rpm for 15 minutes at 4°C.Following centrifugation, 400 μL of supernatant was transferred to a fresh tube and dried in a vacuum concentrator at 37°C.The dried samples were reconstituted in 200 μL of 50% acetonitrile by sonication on ice for 10 minutes.The reconstituted samples were then centrifuged at 13,000 rpm for 15 minutes at 4°C, and 75 μL of fecal supernatant was transferred to a fresh glass vial for LC-MS analysis.To create an overall quality control sample, an equal aliquot of the supernatant from all samples was mixed.

Untargeted metabolomics by high-performance liquid chromatography-mass spectrometry
Metabolic extracts were obtained from serum and fecal samples via methanol protein precipitation, followed by subsequent processing and analysis.A Waters ACQUITY UPLC HSS T3 column, measuring 100 mm × 2.1 mm with a particle size of 1.8 micrometers, was maintained at 45 degrees Celsius.The mobile phase comprised A (water containing 0.1% formic acid) and B (methanol), flowing at a rate of 0.35 mL/min.
Analysis was conducted using a Thermo Fisher UltiMate 3000 ultra-high-performance liquid chromatography system coupled with a Thermo Fisher Q-Exactive Orbitrap mass spectrometer.Both positive and negative ion scanning modes were employed for comprehensive spectrum signal acquisition.Proteo Wizard software was utilized to convert the MS raw data file to mzXML format, followed by processing using XCMS (V.3.2).Processing included peak deconvolution, alignment, and integration, with Minfrac and cutoff values set at 0.5 and 0.3, respectively.Metabolite identification was facilitated using an internal MS2 database.

Metabolomics analysis
LC-MS/MS analyses were conducted utilizing an Agilent Technologies UHPLC System (1290, Santa Clara, CA), equipped with a UPLC BEH Amide column (2.1 × 100 mm, 1.7 μm, Waters).The elution gradient was set as follows: from 0 to 0.5 min, 95% B; from 0.5 to 7.0 min, linearly decreased from 95% to 65% B; from 7.0 to 8.0 min, reduced to 40% B; held at 40% B from 8.0 to 9.0 min; then quickly increased to 95% B from 9.0 to 9.1 min, and maintained at 95% B until 12.0 min.The column was maintained at 25°C, with the autosampler temperature set at 4°C.Injection volumes of 2 μL were used for both ESI + and ESI -modes.MSI data acquisition in the 60-1200 Da range was performed using a 6550 QTOF mass spectrometer (Agilent Technologies).
Additionally, MS/MS spectra acquisition during LC/MS experiments was facilitated using a Triple TOF 6600 mass spectrometer (AB Sciex) through information-dependent acquisition (IDA).The AnalystTF 1.7 software (AB Sciex) continuously evaluated full scan survey MS data to trigger MS/MS spectra collection based on predefined criteria.
Each acquisition cycle targeted the 12 most intense precursor ions exceeding an intensity threshold of 100 units, with a collision energy of 30 eV and a cycle time of 0.56 seconds.ESI source parameters were set as follows: Gas 1 at 60 psi, Gas 2 at 30 The mice would receive a daily topical dose of 62.5 mg IMQ cream (5%) (Aldara, 3 M Pharmaceuticals, MN) on their shaved back for six consecutive days.The mice were assigned to each group randomly, and the investigators were blinded to the group allocation during the experiment.Based on the scoring system called PASI, we scored erythema, scaling, and thickness on the scores from 0 to 4: none 0; slight 1; moderate 2; marked 3; very marked 4.After the mice were sacrificed, the lymph nodes, spleen tissues, and skin samples were collected for flow cytometry.On day 7，mice were euthanized, and the spleen tissues and skin lesion were harvested to analyze the frequency of immune cells by flow cytometry analysis.Total RNA from skin lesion, splenic cells and Spleen CD4 + T cells were extracted to analyze the mRNA expression level of p16 and p21 by RT-qPCR.

CD4 + T cells sorted from spleens of mice
Mouse spleen cells were isolated and ground to get lymphocytes.CD4 + cells were sorted by magnetic beads according to the protocol (Miltenyi 130-117-043).Trizol lysed CD4 cells according to the protocol for subsequent RT-qPCR experiments.
The immune subtypes and their corresponding molecular markers on each panel Human:

Data analysis
In metabolomics data analysis, we initially utilized Proteo Wizard to convert MS raw data files to mzXML format, followed by processing using XCMS (version 3.2) in the R environment.The processing steps involved peak deconvolution, alignment, and integration, with Minfrac and cutoff values set at 0.5 and 0.3, respectively.Subsequently, we employed an internal MS2 database for metabolite identification.To assess the stability of the dataset, we employed Pearson correlation coefficients based on relative quantification of QC samples as a standard metric.The KEGG and HMDB (https://hmdb.ca/metabolites) were used for metabolites annotation and pathway analysis.Correlation analysis of the metabolites with clinical parameters was performed using SPSS.

Figure S2 .
Figure S2.The effects of coffee consumption on T cells.Statistical analysis of the frequency of each T subset and CD57 + T cells from PBMCs of non-coffee consumption consumers (n = 62) and habitual coffee consumers (n = 24).Bars represent the mean ± SEM.

Figure S15 .
Figure S15.The correlation of serum ESI -(A) and ESI + (B) QC samples in the habitual coffee drinkers, serum ESI -(C) and ESI + (D) QC samples and stool ESI -(E) and ESI + (F) QC samples in the short-term coffee drinkers.ESI-, negative electrospray ionization; ESI + , positive electrospray ionization.QC, quality control.

Figure S16 .
Figure S16.The PCA score plots for all samples containing QC samples.QC samples were closely clustered in the principal component analysis of serum samples ESI -and ESI + (A and B) and stool samples ESI -and ESI + (C and D).QC samples: red dots.PCA, principal component analysis.ESI -, negative electrospray ionization; ESI + , positive electrospray ionization.